This article presents the results of a study on spatio-temporal images to evaluate their performances for video-to-shots segmentation purposes. Some shots segmentation methods involve spatio-temporal images that are computed by a projection of successive video frames over the X or Y-axis. On these projections, transition effects and motion are supposed to have different characteristics. Whereas cuts can be easily recognized, the main problem remains in determining a measure that discriminates motions from gradual transition effects. In this article, the quality of transition detections based on line similarity of spatio-temporal images is studied. The probability functions of several measures are estimated to determine which one produce the lowest detection error rate. These distributions are computed on four classes of events: intra shot sequences without motion, sequences with cuts, sequences with fades and sequences with motion. A line matching is performed, based on correlation estimations between projection lines. To separate these classes, we estimate first the density probability functions of the correlation between consecutive lines for each class. For different line segment sizes, the experimental results prove that the class separation can not be clearly produced. To take into account the evolution of the correlation and because we try to detect some particular types of boundaries, we then consider ratios between statistic moments. There are computed over a subset of correlation values. The results show that used measures, based on the matching of projection lines, can not discriminate between motion and fade. Only a subset of motions will be differentiated from gradual transitions. Therefore previous measures should be combined with methods that produce complementary results. Such a method could be a similar measure based on correlation between spatial-shifted segments.